Sisi Liu

Dr Sisi Liu

Online Course Facilitator

Adelaide University Online and Learning Futures

Academic

Dr Sisi Liu is an Online Course Facilitator(OCF) for the Bachelor of IT & Data Analytics degree at UniSA Online. She received her PhD from James Cook University in 2020 and was awarded the International Research Training Program Scholarship(IRTPS) funded by the Australian Government – Department of Education and Training to support her doctoral research project. 
She has a comprehensive experience in teaching and research. Before joining UniSA Online, she had been working as a casual academic at James Cook University for more than four years. Teaching involved activities mainly reflected in the course tutoring, ATAS individual mentoring, and online degree lecturing. Courses that she had facilitated cover the areas of Information Processing and Visualisation, Programming, Database Modelling and Systems, Data Management and Privacy. 
She is also an active researcher in the field of data mining and machine learning and have published several articles in the leading journals and international conferences, such as Knowledge-based Systems and Expert Systems with Applications, during the four-years’ research candidature.
Her current research interests include Sentiment Analysis, Natural Language Processing, Computational Linguistics, Deep Learning.

Sentiment Analysis

Natural Language Processing

Text Mining

Deep Learning

Year Citation
2023 Abadia, R., Liu, S., & Sun, Q. (2023). High academic performance and engagement behaviours in online learning environments. IADIS International Journal on Computer Science and Information Systems, 18(2), 1-21.
2021 Liu, S., & Lee, I. (2021). Sequence encoding incorporated CNN model for Email document sentiment classification. Applied Soft Computing, 102(107104), 1-14.
DOI
2020 Liu, S., Lee, K., & Lee, I. (2020). Document-level multi-topic sentiment classification of Email data with BiLSTM and data augmentation. Knowledge-Based Systems, 197(105918), 1-11.
DOI
2019 Liu, S., & Lee, I. (2019). Extracting features with medical sentiment lexicon and position encoding for drug reviews. Health Information Science and Systems, 7(11), 1-10.
DOI
2018 Liu, S., & Lee, I. (2018). Email sentiment analysis through k-means labeling and support vector machine classification. Cybernetics and Systems, 49(3), 181-199.
DOI
2018 Liu, S., & Lee, I. (2018). Discovering sentiment sequence within email data through trajectory representation. Expert Systems with Applications: an international journal, 99, 1-11.
DOI

Year Citation
2023 Abadia, R., Liu, S., & Sun, Q. (2023). High-performing online student behaviours. In IADIS International Conference Sustainability, Technology and Education 2023 (pp. 27-34). US: International Association for Development of the Information Society.
2021 Abadia, R., & Liu, S. (2021). Low adoption of adaptive learning systems in higher education and how can it be increased in fully online courses. In M. M. T. Rodrigo, S. Iyer, & A. Mitrovic (Eds.), 29th International Conference on Computers in Education (ICCE 2021): conference proceedings Vol. 1 (pp. 569-578). Taiwan: Asia-Pacific Society for Computers in Education.
Scopus1
2018 Liu, S., & Lee, I. (2018). Sentiment classification with medical word embeddings and sequence representation for drug reviews. In S. Siuly, I. Lee, Z. Huang, R. Zhou, H. Wang, & W. Xiang (Eds.), LNCS 11148: Health Information Science: 7th International Conference, HIS 2018 Cairns, QLD, Australia, October 5–7, 2018 Proceedings Vol. 11148 (pp. 75-86). Germany: Springer.
DOI
2016 Liu, S., Cai, G., & Lee, I. (2016). Sentiment clustering with topic & temporal information from large email dataset. In J. C. Park, & J. Chung (Eds.), Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016 (pp. 363-371). South Korea: Kyung Hee University.
2015 Liu, S., & Lee, I. (2015). A hybrid sentiment analysis framework for large email data. In L. O’Conner (Ed.), Proceedings - The 2015 10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015 (pp. 324-330). US: IEEE.
DOI

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